Sherkatghanad Zeinab, Akhondzadeh Mohammadsadegh, Salari Soorena, Zomorodi-Moghadam Mariam, Abdar Moloud, Acharya U Rajendra, Khosrowabadi Reza, Salari Vahid
Department of Physics, Isfahan University of Technology, Isfahan, Iran.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
Front Neurosci. 2020 Jan 14;13:1325. doi: 10.3389/fnins.2019.01325. eCollection 2019.
Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD and control subjects based on the patterns of functional connectivity. Our experimental outcomes indicate that the proposed model is able to detect ASD correctly with an accuracy of 70.22% using the ABIDE I dataset and the CC400 functional parcellation atlas of the brain. Also, the CNN model developed used fewer parameters than the state-of-art techniques and is hence computationally less intensive. Our developed model is ready to be tested with more data and can be used to prescreen ASD patients.
卷积神经网络（CNN）在语音识别、图像分类、汽车软件工程和神经科学领域取得了重大进展。这一令人瞩目的进展很大程度上归功于算法突破、计算资源改进以及大量数据的获取。在本文中，我们专注于使用带有脑成像数据集的CNN自动检测自闭症谱系障碍（ASD）。我们使用来自名为自闭症脑成像交换（ABIDE）的多站点数据集的最常见静息态功能磁共振成像（fMRI）数据来检测ASD患者。所提出的方法能够根据功能连接模式对ASD患者和对照受试者进行分类。我们的实验结果表明，使用ABIDE I数据集和大脑的CC400功能分区图谱，所提出的模型能够以70.22%的准确率正确检测ASD。此外，所开发的CNN模型使用的参数比现有技术更少，因此计算强度更低。我们开发的模型已准备好使用更多数据进行测试，并可用于对ASD患者进行预筛查。
